Moskal P.D., Dziuban C.D., Picciano A.G. Data Analytics and Adaptive Learning: Research Perspectives

New York: Routledge, 2023. — 364 p.Data Analytics and Adaptive Learning offer new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K – 12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.Endorsement Page.Data analytics and adaptive learning: Increasing the odds.
Analytics.
What we want versus what we have: Transforming teacher performance analytics to personalize professional development.
System-wide momentum.
A precise and consistent early warning system for identifying at-risk students.
Predictive analytics, artificial intelligence, and the impact of delivering personalized support to students from underserved backgrounds.
Predicting student success with self-regulated behaviors: A seven-year data analytics study on a Hong Kong University English Course.
Back to Bloom: Why theory matters in closing the achievement gap.
The metaphors we learn by: Toward a philosophy of learning analytics.
Adaptive Learning.
A cross-institutional survey of the instructor use of data analytics in adaptive courses.
Data analytics in adaptive learning for equitable outcomes.
Banking on adaptive questions to nudge student responsibility for learning in general chemistry.
Three-year experience with adaptive learning: Faculty and student perspectives.
Analyzing question items with limited data.
When adaptivity and universal learning design are not enough: Bayesian network recommendations for tutoring.
Organizational Transformation.
Sprint to 2027: Corporate analytics in the digital age.
Academic digital transformation: Focused on data, equity, and learning science.
Closing.
Future technological trends and research.